Joint Depth Understanding.
Experiments REF , REF  and REF  in Table REF  show the detection performance with separate depth estimation from BTS [1]}, DORN [2]}, and CaDDN respectively. The depth maps from BTS [1]} and DORN [2]} are converted to depth bin indices using LID discretization as outlined in Section REF , and converted to a one-hot encoding to generate the depth distributions \(\mathbf {D}\) . The one-hot encoding places the image feature at a single depth bin indicated by the input depth map when generating frustum features \(\mathbf {G}\) . We constuct an equivalent version of CaDDN that selects a single depth bin for each pixel, by selecting the bin with highest probability for each distribution in \(\mathbf {D}\) . Experiment REF  shows improved performance (+2.97%, +1.45%, +1.86%) when depth estimation and object detection are performed jointly, which we attribute to the well-known benefits of end-to-end learning for 3D detection.
